Systems and methods for image correction in positron emission tomography

ABSTRACT

System for image correction in PET is provided. The system may acquire a PET image and a CT image of a subject. The system may generate, based on the PET image and the CT image, an attenuation-corrected PET image of the subject by application of an attenuation correction model. The attenuation correction model may be a trained cascaded neural network including a trained first model and at least one trained second model downstream to the trained first model. During the application of the attenuation correction model, an input of each of the at least one trained second model may include the PET image, the CT image, and an output image of a previous trained model that is upstream and connected to the trained second model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority of Chinese Patent Application No.201811630048.5, filed on Dec. 28, 2018, the contents of which are herebyincorporated by reference.

TECHNICAL FIELD

The disclosure generally relates to image processing, and moreparticularly relates to systems and methods for image correction inpositron emission tomography (PET) image reconstruction.

BACKGROUND

PET is widely used in disease diagnosis and/or treatment for variousmedical conditions (e.g., tumors, coronary heart diseases, or braindisease). During PET imaging, a radioactive tracer may be injected intoa subject (e.g., a patient) without changing the physiological state ofthe subject. The radioactive tracer may participate in the physiologicalmetabolism of the subject, and a PET scan may be performed to detectgamma rays emitted from the subject. A PET image of the subject may thenbe reconstructed based on PET data collected in the PET scan to evaluatethe physiology (or functionality) functions of the subject.

Generally, during the reconstruction of the PET image, a series of dataprocessing operations (e.g., an attenuation correction, a scattercorrection, a statistical iterative reconstruction operation) may beperformed on the PET data. Such operations may require mathematicalmodeling with a large amount of calculation. The reconstruction of thePET image may be time-consuming, for example, take 2-3 minutes, and areal-time browsing of the PET image can be hardly achieved. In addition,in the reconstruction process of PET image, a user may be unable tocontrol the scan process in real-time because of lack of informationregarding the current operating condition of the PET scanner and/or areal-time PET image, which may reduce the efficiency of the PET scan andresult in a waste of resources. Therefore, it is desired to providesystems and methods for image correction in PET image reconstruction,thereby achieving instant browsing of PET images during the PET scan,improving the scan efficiency, and save resources.

SUMMARY

In an aspect of the present disclosure, a system for image correction inpositron emission tomography (PET) is provided. The system may includeat least one storage device including a set of instructions, and atleast one processor configured to communicate with the at least onestorage device. When executing the set of instructions, the system maybe configured to direct the system to perform the following operations.The system may acquire a PET image and a computed tomography (CT) imageof a subject. The system may also generate, based on the PET image andthe CT image, an attenuation-corrected PET image of the subject byapplication of an attenuation correction model. The attenuationcorrection model may be a trained cascaded neural network including aplurality of trained models that are sequentially connected. Theplurality of trained models may include a trained first model and atleast one trained second model downstream to the trained first model.During the application of the attenuation correction model, an input ofeach of the at least one trained second model may include the PET image,the CT image, and an output image of a previous trained model that isupstream and connected to the trained second model.

In some embodiments, to generate an attenuation-corrected PET image ofthe subject by application of an attenuation correction model, thesystem may preprocess the CT image and the PET image. The system maygenerate a concatenated image by concatenating the preprocessed CT imageand the preprocessed PET image. The system may obtain a preliminaryattenuation-corrected PET image by inputting the concatenated image intothe attenuation correction model. The system may also generate theattenuation-corrected PET image by processing the preliminaryattenuation-corrected PET image.

In some embodiments, to preprocess the CT image and the PET image, thesystem may register the CT image with the PET image. The system maygenerate a resampled CT image and a resampled PET image by resamplingthe registered CT image and the registered PET image. Each of theresampled CT image and the resampled PET image may have a preset imageresolution. The system may also generate the preprocessed CT image andthe preprocessed PET image by normalizing the resampled CT image and theresampled PET image.

In some embodiments, to generate the attenuation-corrected PET image byprocessing the preliminary attenuation-corrected PET image, the systemmay denormalize the preliminary attenuation-corrected PET image. Thesystem may also generate the attenuation-corrected PET image byresampling the denormalized preliminary attenuation-corrected PET image.The attenuation-corrected PET image and the PET image may have a sameimage resolution.

In some embodiments, the attenuation correction model may be trainedusing a plurality of sample attenuation-corrected PET images, and thedenormalization of the preliminary attenuation-corrected PET image maybe performed based on a mean value and a standard deviation of theplurality of sample attenuation-corrected PET images.

In some embodiments, to acquire a PET image and a CT image of a subject,the system may acquire CT image data and PET image data of the subjectby performing a CT scan and a PET scan of the subject. The system mayreconstruct, based on the CT image data, the CT image. The system mayreconstruct, based on the PET image data, a preliminary PET image. Thesystem may also generate the PET image by performing a random correctionand a detector normalization on the preliminary PET image.

In some embodiments, at least one of the plurality of trained models maybe a convolutional neural network (CNN) model or a generativeadversarial network (GAN) model.

In some embodiments, the generation the attenuation-corrected PET imageof the subject may be performed within 1 second.

In another aspect of the present disclosure, a system for generating anattenuation correction model is provided. The system may include atleast one storage device including a set of instructions, and at leastone processor configured to communicate with the at least one storagedevice. When executing the set of instructions, the system may beconfigured to direct the system to perform the following operations. Thesystem may acquire a plurality of training samples, each of theplurality of training samples including a sample positron-emissiontomography (PET) image of a sample subject, a sample computed tomography(CT) image of the sample subject, and a sample attenuation-corrected PETimage corresponding to the sample PET image. The system may alsogenerate the attenuation correction model by training a cascaded neuralnetwork using the plurality of training samples. The cascaded neuralnetwork may include a plurality of sequentially connected models. Theplurality of models may include a first model and at least one secondmodel downstream to the first model. During the training of the cascadedneural network, each of the at least one second model may be trainedbased on the plurality training samples and one or more models in thecascaded neural network upstream to the second model.

In some embodiments, the plurality of models may be trained in parallelduring the training of the cascaded neural network. To train thecascaded neural network, The system may initialize parameter values ofthe cascaded neural network. The system may also train the cascadedneural network by iteratively updating the parameter values of thecascaded neural network based on the plurality of training samples.

In some embodiments, to iteratively update the parameter values of thecascaded neural network, the system may perform an iterative operationincluding one or more iterations. Each of at least one iteration of theiterative operation may include generating a predictedattenuation-corrected PET image by application of an updated cascadedneural network determined in a previous iteration for each of at leastsome of the plurality of training samples. Each of at least oneiteration of the iterative operation may also include determining, basedon the predicted attenuation-corrected PET image and the sampleattenuation-corrected PET image of each of the at least some of theplurality of training samples, an assessment result of the updatedcascaded neural network. Each of at least one iteration of the iterativeoperation may also include further updating the parameter values of theupdated cascaded neural network to be used in a next iteration based onthe assessment result. During the application of the updated cascadedneural network to a training sample, each second model of the updatedcascaded neural network may be configured to receive the training sampleand an output image of a previous model that is upstream and connectedto the second model in the updated cascaded neural network, and thepredicted attenuation-corrected PET image may be an output image of alast second model of the sequentially connected models in the updatedcascaded neural network.

In some embodiments, the assessment result may be determined based on atleast one of a difference between the predicted attenuation-correctedPET image and the sample attenuation-corrected PET image of each of atleast some of the plurality of training samples, or a time needed forthe updated cascaded neural network to generate the predictedattenuation-corrected PET image of each of the at least some of theplurality of training samples.

In some embodiments, to determine an assessment result of the updatedcascaded neural network, for each of the plurality of models in theupdated cascaded neural network, the system may determine, based on thesample attenuation-corrected PET image and an output image of the modelcorresponding to each of the at least some of the plurality of trainingsamples, a value of a loss function corresponding to the model. Thesystem may also determine, based on the values of the loss functions ofthe plurality of models, the assessment result.

In some embodiments, the parameter values of the cascaded neural networkmay include parameter values of each of the plurality of models, and thefurther updating the parameter values of the updated cascaded neuralnetwork based on the assessment result includes updating the parametervalues of the model based on the value of the corresponding lossfunction for each of the plurality of models in the updated cascadedneural network.

In some embodiments, the training the cascaded neural network mayinclude sequentially training the plurality of models. The first modelmay be trained using the plurality of training samples. Each of the atleast one second model may be trained using the plurality of trainingsamples and one or more trained models generated before the training ofthe second model.

In some embodiments, for each of the at least one second model, thetraining the second model may include generating a preliminary image byapplication of the one or more trained models generated before thetraining of the second model for each of the plurality of trainingsamples. The training the second model may include initializingparameter values of the second model. The training the second model mayalso include training the second model by iteratively updating theparameter values of the second model based on the plurality of trainingsamples and the corresponding preliminary images.

In some embodiments, the training the second model may include a seconditerative operation including one or more iterations. Each of at leastone iteration of the second iterative operation may include for each ofat least some of the plurality of training samples, generating an outputimage of the second model by inputting the training sample and thecorresponding preliminary image into an updated second model determinedin a previous iteration. Each of at least one iteration of the seconditerative operation may include determining, based on the output imageof the updated second model and the sample attenuation-corrected PETimage corresponding to each of the at least some of the plurality oftraining samples, a second assessment result. Each of at least oneiteration of the second iterative operation may also include furtherupdating the parameter values of the updated second model to be used ina next iteration based on the second assessment result.

In some embodiments, to generate the attenuation correction model bytraining a cascaded neural network using the plurality of trainingsamples, for each of the plurality of training samples, the system maypreprocess the sample PET image, the sample CT image, and the sampleattenuation-corrected PET image of the training sample, and generate asample concentrated image by concatenating the preprocessed sample CTimage and the preprocessed sample PET image of the training sample. Thesystem may also generate the attenuation correction model by trainingthe cascaded neural network using the sample concatenated images and theplurality of preprocessed sample attenuation-corrected PET images.

In some embodiments, at least one of the plurality of models may be aconvolutional neural network (CNN) model or a generative adversarialnetwork (GAN) model.

In another aspect of the present disclosure, a method for imagecorrection in positron emission tomography (PET) is provided. The methodmay be implemented on a computing device having at least one processorand at least one storage device. The method may include acquiring a PETimage and a computed tomography (CT) image of a subject. The method mayinclude generating, based on the PET image and the CT image, anattenuation-corrected PET image of the subject by application of anattenuation correction model. The attenuation correction model may be atrained cascaded neural network including a plurality of trained modelsthat are sequentially connected. The plurality of trained models mayinclude a trained first model and at least one trained second modeldownstream to the trained first model. During the application of theattenuation correction model, an input of each of the at least onetrained second model may include the PET image, the CT image, and anoutput image of a previous trained model that is upstream and connectedto the trained second model. The method may also include transmittingthe attenuation-corrected PET image of the subject to a terminal fordisplay.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities, andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not to scale. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing device may be implemented accordingto some embodiments of the present disclosure;

FIG. 3 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device according to some embodimentsof the present disclosure;

FIG. 4A and FIG. 4B are block diagrams illustrating exemplary processingdevices according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for generatingan attenuation-corrected PET image of a subject according to someembodiments of the present disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for generatingan attenuation-corrected PET image according to some embodiments of thepresent disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for generatingan attenuation correction model according to some embodiments of thepresent disclosure;

FIG. 8 is a flowchart illustrating an exemplary process for training acascaded neural network according to some embodiments of the presentdisclosure;

FIG. 9 is a flowchart illustrating an exemplary process for training asecond model of a cascaded neural network according to some embodimentsof the present disclosure;

FIG. 10 illustrates an exemplary cascaded neural network according tosome embodiments of the present disclosure;

FIG. 11A illustrates an exemplary CT image of a portion of a patientaccording to some embodiments of the present disclosure;

FIG. 11B illustrates an exemplary PET image of the portion of thepatient in FIG. 11A according to some embodiments of the presentdisclosure;

FIG. 11C illustrates an exemplary attenuation-corrected PET imagecorresponding to the PET image in FIG. 11B according to some embodimentsof the present disclosure;

FIG. 12A illustrates an exemplary CT image of a portion of a patientaccording to some embodiments of the present disclosure;

FIG. 12B illustrates an exemplary PET image of the portion of thepatient in FIG. 12A according to some embodiments of the presentdisclosure;

FIG. 12C illustrates an exemplary attenuation-corrected PET imagecorresponding to the PET image in FIG. 12B according to some embodimentsof the present disclosure;

FIG. 13A illustrates an exemplary CT image of a portion of a patientaccording to some embodiments of the present disclosure;

FIG. 13B illustrates an exemplary PET image of the portion of thepatient in FIG. 13A according to some embodiments of the presentdisclosure; and

FIG. 13C illustrates an exemplary attenuation-corrected PET imagecorresponding to the PET image in FIG. 13B according to some embodimentsof the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, sections or assembly of differentlevels in ascending order. However, the terms may be displaced byanother expression if they achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 210 as illustrated in FIG. 2) may beprovided on a computer-readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedin connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks,but may be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items. The term “image” in the present disclosure isused to collectively refer to image data (e.g., scan data) and/or imagesof various forms, including a two-dimensional (2D) image, athree-dimensional (3D) image, a four-dimensional (4D) image, etc. Theterm “pixel” and “voxel” in the present disclosure are usedinterchangeably to refer to an element of an image.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

Provided herein are systems and methods for non-invasive biomedicalimaging, such as for disease diagnostic or research purposes. In someembodiments, the systems may include a single modality imaging systemand/or a multi-modality imaging system. The single modality imagingsystem may include, for example, a PET system and a computed tomography(CT) system. The multi-modality imaging system may include, for example,a positron emission tomography-computed tomography (PET-CT) system, etc.It should be noted that the imaging system described below is merelyprovided for illustration purposes, and not intended to limit the scopeof the present disclosure.

The term “imaging modality” or “modality” as used herein broadly refersto an imaging method or technology that gathers, generates, processes,and/or analyzes imaging information of an object. The object may includea biological object and/or a non-biological object. The biologicalobject may be a human being, an animal, a plant, or a portion thereof(e.g., a cell, a tissue, an organ, etc.). In some embodiments, theobject may be a man-made composition of organic and/or inorganic mattersthat are with or without life. The term “object” or “subject” are usedinterchangeably.

An aspect of the present disclosure relates to systems and methods forimage correction in PET. The systems and methods may acquire a PET imageand a CT image of a subject. Based on the PET image and the CT image,the systems and methods may generate an attenuation-corrected PET imageof the subject by application of an attenuation correction model. Theattenuation correction model may be a trained cascaded neural networkincluding a plurality of trained models that are sequentially connected.The plurality of trained models may include a trained first model and atleast one trained second model downstream to the trained first model.During the application of the attenuation correction model, an input ofeach of the at least one trained second model may include the PET image,the CT image, and an output image of a previous trained model that isupstream and connected to the trained second model.

In some embodiments, the systems and methods for PET image correctionmay be applied during a PET scan of a subject. By applying theattenuation correction model, an attenuation corrected PET image of thesubject may be reconstructed in a relatively high speed and an instantbrowsing of the attenuation-corrected PET image may be achieved, whichmay help a technician or doctor to adjust the scan process in real-timeand improve the scan efficiency. As used herein, an instant (orreal-time) browsing of an image during a PET scan refers to that theimage is reconstructed and displayed on a terminal device when the PETscan is still performed or immediately after the PET scan is finished.For example, during the PET scan, PET data may be collected andtransmitted to a processing device continuously or intermittently (e.g.,periodically). The processing device may generate anattenuation-corrected PET image based on the PET data and update theattenuation-corrected PET image continuously or intermittently (e.g.,periodically), wherein the generated or updated attenuation-correctedPET image may be displayed on the terminal device in real-time orsubstantially real-time. As another example, after the PET scan isfinished, the processing device may generate an attenuation-correctedPET image based on PET data collected in the whole PET scan immediately(e.g., within a certain threshold period, such as 1 second, 2 seconds, 3seconds, after the PET scan is finished), so as to achieve an instantbrowsing of the attenuation-corrected PET image.

Another aspect of the present disclosure relates to systems and methodsfor generating an attenuation correction model. The systems and methodsmay acquire a plurality of training samples. Each of the plurality oftraining samples may include a sample PET image of a sample subject, asample CT image of the sample subject, and a sampleattenuation-corrected PET image corresponding to the sample PET image.The systems and methods may also generate the attenuation correctionmodel by training a cascaded neural network using the plurality oftraining samples. The cascaded neural network may include a plurality ofsequentially connected models. The plurality of models may include afirst model and at least one second model downstream to the first model.During the training of the cascaded neural network, each of the at leastone second model may be trained based on the plurality training samplesand one or more models in the cascaded neural network upstream to thesecond model.

According to some embodiments of the present disclosure, the cascadedneural network may be trained using the sample PET image and the sampleCT image of each training sample. For a certain training sample, thecorresponding sample CT image may have a relatively higher imagecontrast and/or a relatively higher density resolution, and thecorresponding sample PET image may have more functional information. Thesample CT image may facilitate the reconstruction of one or morestructures (e.g., lung region, soft tissue, human epidermal tissue,etc.) in the sample PET image. By using both the sample PET image andthe sample PET image as a training sample, the trained cascaded neuralnetwork (also referred to as the attenuation correction model) maycombine features of the two images, thereby having a higher accuracy andreliability. In addition, in some embodiments, the reliability of theattenuation correction model may be further improved by adopting a deepauto-context learning strategy in training the cascaded neural network.

In some embodiments, the models of the cascaded neural network may betrained simultaneously and share one or more parameters (e.g., weights),which may reduce the size of the corresponding attenuation correctionmodel and/or facilitate image reconstruction in applying the attenuationcorrection model. Optionally, the models of the cascaded neural networkmay include a plurality of 1*1 convolutional blocks, which may reducethe complexity of the attenuation correction model and furtherfacilitate image reconstruction in applying the attenuation correctionmodel. In addition, in applying the attenuation correction model, aninput image (e.g., a PET image and/or a CT image of a subject) may beresampled to generate a resampled input image having an image resolutionthat corresponds to the attenuation correction model.

FIG. 1 is a schematic diagram illustrating an exemplary imaging system100 according to some embodiments of the present disclosure. In someembodiments, the imaging system 100 may be a single-modality system(e.g., a PET system, a CT system) or a multi-modality system (e.g., aPET-CT system, such as a 2 m PET-CT). In some embodiments, the imagingsystem 100 may include modules and/or components for performing imagingand/or related analysis.

Merely by way of example, as illustrated in FIG. 1, the imaging system100 may include an imaging device 110, a processing device 120, astorage device 130, one or more terminals 140, and a network 150. Thecomponents in the imaging system 100 may be connected in various ways.Merely by way of example, the imaging device 110 may be connected to theprocessing device 120 through the network 150 or directly as illustratedin FIG. 1. As another example, the terminal(s) 140 may be connected tothe processing device 120 via the network 150 or directly as illustratedin FIG. 1.

The imaging device 110 may be configured to acquire imaging datarelating to a subject. The imaging data relating to a subject mayinclude an image (e.g., an image slice), projection data, or acombination thereof. In some embodiments, the imaging data may betwo-dimensional (2D) imaging data, three-dimensional (3D) imaging data,four-dimensional (4D) imaging data, or the like, or any combinationthereof. The subject may be biological or non-biological. For example,the subject may include a patient, a man-made object, etc. As anotherexample, the subject may include a specific portion, organ, and/ortissue of the patient. For example, the subject may include the head,the neck, the thorax, the heart, the stomach, a blood vessel, softtissue, a tumor, nodules, or the like, or any combination thereof.

In some embodiments, the imaging device 110 may include a PET device, aCT device, or a PET-CT device. The PET device may scan the subject or aportion thereof that is located within its detection region and generateprojection data relating to the subject or the portion thereof. The PETdevice may include a gantry, a detector, an electronics module, and/orother components not shown. The gantry may support one or more parts ofthe PET device, for example, the detector, the electronics module,and/or other components. The detector may detect radiation photons(e.g., γ photons) emitted from the subject being examined. Theelectronics module may collect and/or process electrical signals (e.g.,scintillation pulses) generated by the detector. The electronics modulemay convert an analog signal (e.g., an electrical signal generated bythe detector) relating to a radiation photon detected by the detector toa digital signal relating to a radiation event. As used herein, aradiation event may refer to an interaction between a radiation photonemitted from a subject and impinging on and detected by the detector. Apair of radiation photons (e.g., γ photons) interacting with twodetector blocks along a line of response (LOR) within a coincidence timewindow may be determined as a coincidence event. A portion of theradiation photons (e.g., γ photons) emitted from a subject beingexamined may interact with tissue in the subject. The radiation photons(e.g., γ photons) interacting with tissue in the subject may bescattered or otherwise change its trajectory, that may affect the numberor count of radiation photons (e.g., γ photons) detected by two detectorblocks along a line of response (LOR) within a coincidence time windowand the number or count of coincidence events. The CT device may scanthe subject or a portion thereof that is located within its detectionregion and generate CT image data relating to the subject or the portionthereof. The CT image data may be acquired by the CT device via scanningthe subject using a radiation source (e.g., an X-ray source). The PET-CTdevice may include a PET component and a CT component. For example, thePET component may be used to provide functional and metabolicinformation of the subject (e.g., a lesion), and the CT component may beused to provide structural and anatomical information of the subject.

The processing device 120 may process data and/or information obtainedfrom the imaging device 110, the terminal(s) 140, and/or the storagedevice 130. For example, the processing device 120 may correct a PETimage by applying an attenuation correction model. As another example,the processing device 120 may generate the attenuation correction modelby training a cascaded neural network using a plurality of trainingsamples. In some embodiments, the generation and/or updating of theattenuation correction model may be performed on a processing device,while the application of the attenuation correction model may beperformed on a different processing device. In some embodiments, thegeneration of the attenuation correction model may be performed on aprocessing device of a system different from the imaging system 100 or aserver different from a server including the processing device 120 onwhich the application of the attenuation correction model is performed.For instance, the generation of the attenuation correction model may beperformed on a first system of a vendor who provides and/or maintainssuch an attenuation correction model and/or has access to trainingsamples used to generate the attenuation correction model, while imagecorrection based on the provided attenuation correction model may beperformed on a second system of a client of the vendor. In someembodiments, the generation of the attenuation correction model may beperformed online in response to a request for image correction. In someembodiments, the generation of the attenuation correction model may beperformed offline.

In some embodiments, the attenuation correction model may be generatedand/or updated (or maintained) by, e.g., the manufacturer of the imagingdevice 110 or a vendor. For instance, the manufacturer or the vendor mayload the attenuation correction model into the imaging system 100 or aportion thereof (e.g., the processing device 120) before or during theinstallation of the imaging device 110 and/or the processing device 120,and maintain or update the attenuation correction model from time totime (periodically or not). The maintenance or update may be achieved byinstalling a program stored on a storage device (e.g., a compact disc, aUSB drive, etc.) or retrieved from an external source (e.g., a servermaintained by the manufacturer or vendor) via the network 150. Theprogram may include a new model (e.g., a new attenuation correctionmodel) or a portion of a model that substitute or supplement acorresponding portion of the model.

In some embodiments, the processing device 120 may be a computer, a userconsole, a single server or a server group, etc. The server group may becentralized or distributed. In some embodiments, the processing device120 may be local or remote. For example, the processing device 120 mayaccess information and/or data stored in the imaging device 110, theterminal(s) 140, and/or the storage device 130 via the network 150. Asanother example, the processing device 120 may be directly connected tothe imaging device 110, the terminal(s) 140 and/or the storage device130 to access stored information and/or data. In some embodiments, theprocessing device 120 may be implemented on a cloud platform. Merely byway of example, the cloud platform may include a private cloud, a publiccloud, a hybrid cloud, a community cloud, a distributed cloud, aninter-cloud, a multi-cloud, or the like, or any combination thereof.

The storage device 130 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 130 may store dataobtained from the terminal(s) 140 and/or the processing device 120. Forexample, the storage device 130 may store image data (e.g., PET images,CT images, etc.) acquired by the imaging device 110. As another example,the storage device 130 may store one or more algorithms for processingthe image data, an attenuation correction model for image correction,etc. In some embodiments, the storage device 130 may store data and/orinstructions that the processing device 120 may execute or use toperform exemplary methods/systems described in the present disclosure.In some embodiments, the storage device 130 may include a mass storagedevice, a removable storage device, a volatile read-and-write memory, aread-only memory (ROM), or the like, or any combination thereof.Exemplary mass storage devices may include a magnetic disk, an opticaldisk, a solid-state drive, etc. Exemplary removable storage devices mayinclude a flash drive, a floppy disk, an optical disk, a memory card, azip disk, a magnetic tape, etc. Exemplary volatile read-and-writememories may include a random access memory (RAM). Exemplary RAM mayinclude a dynamic RAM (DRAM), a double date rate synchronous dynamic RAM(DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. Exemplary ROM may include a mask ROM(MROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM),an electrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage device 130 may be implemented on a cloud platform. Merely byway of example, the cloud platform may include a private cloud, a publiccloud, a hybrid cloud, a community cloud, a distributed cloud, aninter-cloud, a multi-cloud, or the like, or any combination thereof.

In some embodiments, the storage device 130 may be connected to thenetwork 150 to communicate with one or more other components in theimaging system 100 (e.g., the processing device 120, the terminal(s)140, etc.). One or more components in the imaging system 100 may accessthe data or instructions stored in the storage device 130 via thenetwork 150. In some embodiments, the storage device 130 may be directlyconnected to or communicate with one or more other components in theimaging system 100 (e.g., the processing device 120, the terminal(s)140, etc.). In some embodiments, the storage device 130 may be part ofthe processing device 120.

The terminal(s) 140 may include a mobile device 140-1, a tablet computer140-2, a laptop computer 140-3, or the like, or any combination thereof.In some embodiments, the mobile device 140-1 may include a smart homedevice, a wearable device, a mobile device, a virtual reality device, anaugmented reality device, or the like, or any combination thereof. Insome embodiments, the smart home device may include a smart lightingdevice, a control device of an intelligent electrical apparatus, a smartmonitoring device, a smart television, a smart video camera, aninterphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a bracelet, a footgear,eyeglasses, a helmet, a watch, clothing, a backpack, a smart accessory,or the like, or any combination thereof. In some embodiments, the mobiledevice may include a mobile phone, a personal digital assistant (PDA), agaming device, a navigation device, a point of sale (POS) device, alaptop, a tablet computer, a desktop, or the like, or any combinationthereof. In some embodiments, the virtual reality device and/or theaugmented reality device may include a virtual reality helmet, virtualreality glasses, a virtual reality patch, an augmented reality helmet,augmented reality glasses, an augmented reality patch, or the like, orany combination thereof. For example, the virtual reality device and/orthe augmented reality device may include a Google Glass™, an OculusRift™, a Hololens™, a Gear VR™, etc. In some embodiments, theterminal(s) 140 may be part of the processing device 120.

The network 150 may include any suitable network that can facilitate theexchange of information and/or data for the imaging system 100. In someembodiments, one or more components of the imaging device 110 (e.g., aCT device, a PET device, etc.), the terminal(s) 140, the processingdevice 120, the storage device 130, etc., may communicate informationand/or data with one or more other components of the imaging system 100via the network 150. For example, the processing device 120 may obtainimage data from the imaging device 110 via the network 150. As anotherexample, the processing device 120 may obtain user instructions from theterminal(s) 140 via the network 150. The network 150 may be and/orinclude a public network (e.g., the Internet), a private network (e.g.,a local area network (LAN), a wide area network (WAN)), etc.), a wirednetwork (e.g., an Ethernet network), a wireless network (e.g., an 802.11network, a Wi-Fi network, etc.), a cellular network (e.g., a Long TermEvolution (LTE) network), a frame relay network, a virtual privatenetwork (“VPN”), a satellite network, a telephone network, routers,hubs, switches, server computers, and/or any combination thereof. Merelyby way of example, the network 150 may include a cable network, awireline network, a fiber-optic network, a telecommunications network,an intranet, a wireless local area network (WLAN), a metropolitan areanetwork (MAN), a public telephone switched network (PSTN), a Bluetooth™network, a ZigBee™ network, a near field communication (NFC) network, orthe like, or any combination thereof. In some embodiments, the network150 may include one or more network access points. For example, thenetwork 150 may include wired and/or wireless network access points suchas base stations and/or internet exchange points through which one ormore components of the imaging system 100 may be connected to thenetwork 150 to exchange data and/or information.

It should be noted that the above description of the imaging system 100is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. For example, the imagingsystem 100 may include one or more additional components and/or one ormore components of the imaging system 100 described above may beomitted. Additionally or alternatively, two or more components of theimaging system 100 may be integrated into a single component. Acomponent of the imaging system 100 may be implemented on two or moresub-components.

FIG. 2 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing device 200 may be implementedaccording to some embodiments of the present disclosure. The computingdevice 200 may be used to implement any component of the imaging systemas described herein. For example, the processing device 120 and/or aterminal 140 may be implemented on the computing device 200,respectively, via its hardware, software program, firmware, or acombination thereof. Although only one such computing device is shown,for convenience, the computer functions relating to the imaging system100 as described herein may be implemented in a distributed fashion on anumber of similar platforms, to distribute the processing load. Asillustrated in FIG. 2, the computing device 200 may include a processor210, a storage 220, an input/output (I/O) 230, and a communication port240.

The processor 210 may execute computer instructions (program codes) andperform functions of the processing device 120 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, signals, datastructures, procedures, modules, and functions, which perform particularfunctions described herein. For example, the processor 210 may performattenuation correction on a PET image to generate anattenuation-corrected PET image. As another example, the processor 210may generate an attenuation correction model according to a machinelearning technique. In some embodiments, the processor 210 may performinstructions obtained from the terminal(s) 140. In some embodiments, theprocessor 210 may include one or more hardware processors, such as amicrocontroller, a microprocessor, a reduced instruction set computer(RISC), an application-specific integrated circuits (ASICs), anapplication-specific instruction-set processor (ASIP), a centralprocessing unit (CPU), a graphics processing unit (GPU), a physicsprocessing unit (PPU), a microcontroller unit, a digital signalprocessor (DSP), a field-programmable gate array (FPGA), an advancedRISC machine (ARM), a programmable logic device (PLD), any circuit orprocessor capable of executing one or more functions, or the like, orany combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors. Thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing device 200executes both operation A and operation B, it should be understood thatoperation A and operation B may also be performed by two or moredifferent processors jointly or separately in the computing device 200(e.g., a first processor executes operation A and a second processorexecutes operation B, or the first and second processors jointly executeoperations A and B).

The storage 220 may store data/information obtained from the imagingdevice 110, the terminal(s) 140, the storage device 130, or any othercomponent of the imaging system 100. In some embodiments, the storage220 may include a mass storage device, a removable storage device, avolatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. In some embodiments, the storage 220 maystore one or more programs and/or instructions to perform exemplarymethods described in the present disclosure. For example, the storage220 may store a program for the processing device 120 for performingattenuation correction on a PET image.

The I/O 230 may input or output signals, data, and/or information. Insome embodiments, the I/O 230 may enable user interaction with theprocessing device 120. In some embodiments, the I/O 230 may include aninput device and an output device. Exemplary input devices may include akeyboard, a mouse, a touch screen, a microphone, or the like, or acombination thereof. Exemplary output devices may include a displaydevice, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Exemplary display devices may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), or the like, or a combination thereof.

The communication port 240 may be connected with a network (e.g., thenetwork 150) to facilitate data communications. The communication port240 may establish connections between the processing device 120 and theimaging device 110, the terminal(s) 140, or the storage device 130. Theconnection may be a wired connection, a wireless connection, or acombination of both that enables data transmission and reception. Thewired connection may include an electrical cable, an optical cable, atelephone wire, or the like, or any combination thereof. The wirelessconnection may include a Bluetooth network, a Wi-Fi network, a WiMaxnetwork, a WLAN, a ZigBee network, a mobile network (e.g., 3G, 4G, 5G,etc.), or the like, or any combination thereof. In some embodiments, thecommunication port 240 may be a standardized communication port, such asRS232, RS485, etc. In some embodiments, the communication port 240 maybe a specially designed communication port. For example, thecommunication port 240 may be designed in accordance with the digitalimaging and communications in medicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device 300 according to someembodiments of the present disclosure. In some embodiments, one or morecomponents (e.g., a terminal 140 and/or the processing device 120) ofthe imaging system 100 may be implemented on the mobile device 300.

As illustrated in FIG. 3, the mobile device 300 may include acommunication platform 310, a display 320, a graphics processing unit(GPU) 330, a central processing unit (CPU) 340, an I/O 350, a memory360, and a storage 390. In some embodiments, any other suitablecomponent, including but not limited to a system bus or a controller(not shown), may also be included in the mobile device 300. In someembodiments, a mobile operating system 370 (e.g., iOS, Android, WindowsPhone, etc.) and one or more applications 380 may be loaded into thememory 360 from the storage 390 in order to be executed by the CPU 340.The applications 380 may include a browser or any other suitable mobileapps for receiving and rendering information relating to imageprocessing or other information from the processing device 120. Userinteractions with the information stream may be achieved via the I/O 350and provided to the processing device 120 and/or other components of theimaging system 100 via the network 150.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. The hardware elements, operating systems and programminglanguages of such computers are conventional in nature, and it ispresumed that those skilled in the art are adequately familiar therewithto adapt those technologies to generate an image as described herein. Acomputer with user interface elements may be used to implement apersonal computer (PC) or another type of work station or terminaldevice, although a computer may also act as a server if appropriatelyprogrammed. It is believed that those skilled in the art are familiarwith the structure, programming and general operation of such computerequipment and as a result, the drawings should be self-explanatory.

FIG. 4A and FIG. 4B are block diagrams illustrating exemplary processingdevices 120A and 120B according to some embodiments of the presentdisclosure. In some embodiments, the processing devices 120A and 120Bmay be embodiments of the processing device 120 as described inconnection with FIG. 1. In some embodiments, the processing devices 120Aand 120B may be respectively implemented on a processing unit (e.g., theprocessor 210 illustrated in FIG. 2 or the CPU 340 as illustrated inFIG. 3). Merely by way of example, the processing devices 120A may beimplemented on a CPU 340 of a terminal device, and the processing device120B may be implemented on a computing device 200. Alternatively, theprocessing devices 120A and 120B may be implemented on a same computingdevice 200 or a same CPU 340. For example, the processing devices 120Aand 120B may be implemented on a same computing device 200.

As illustrated in FIG. 4A, the processing device 120A may include anacquisition module 401 and a generation module 402.

The acquisition module 401 may be configured to acquire informationrelating to the imaging system 100. For example, the acquisition module401 may acquire a PET image and a CT image of a subject from a storagedevice (e.g., the storage device 130, the storage 220, etc.). As anotherexample, the acquisition module 401 may acquire CT image data and PETimage data of the subject from a PET-CT scanner (e.g., a 2 m PET-CTscanner) that performs a CT scan and PET scan on the subject. As stillanother example, the acquisition module 401 may acquire an attenuationcorrection model from a storage device (e.g., the storage device 130,the storage 220, etc.) that stores the attenuation correction model.

The generation module 402 may be configured to generate anattenuation-corrected PET image of the subject based on the PET imageand the CT image of the subject. For example, the generation module 402may input the PET image and the CT image into the attenuation correctionmodel, and the attenuation correction model may output theattenuation-corrected PET image. As another example, the generationmodule 402 may preprocess the CT image and the PET image of the subject.The generation module 402 may generate a concatenated image byconcatenating the preprocessed CT image and the preprocessed PET image.The generation module 402 may obtain a preliminary attention-correctedPET image by inputting the concatenated image into the attenuationcorrection model, and generate the attenuation-corrected PET image byprocessing the preliminary attention-corrected PET image. Moredescriptions regarding the attenuation correction model and thegeneration of the attenuation-corrected PET image may be found elsewherein the present disclosure (e.g., FIGS. 5-6 and the descriptionsthereof).

As illustrated in FIG. 4B, the processing device 120B may include anacquisition module 403 and a training module 404.

The acquisition module 403 may be configured to acquire informationand/or data for generating an attenuation correction model. For example,the acquisition module 403 may acquire a plurality of training samples.Each of the plurality of training samples may include a sample PET imageof a sample subject, a sample CT image of the sample subject, and asample attenuation-corrected PET image corresponding to the sample PETimage. In some embodiments, the training samples may be previouslygenerated and stored in a storage device (e.g., the storage device 130,the storage 220, the storage 390, or an external database). Theacquisition module 403 may directly acquire the plurality of trainingsamples from the storage device. In some embodiments, at least a portionof the training samples may be generated by the acquisition module 403.For example, the acquisition module 403 ma acquire sample image datafrom a storage device (e.g., the storage device 130, the storage 220,the storage 390, or an external database) and reconstruct the trainingsamples based on the sample image data. More descriptions regarding theacquisition of the training samples may be found elsewhere in thepresent disclosure (e.g., operation 701 in FIG. 7 and the descriptionsthereof).

The training module 404 may be configured to generate the attenuationcorrection model by training a cascaded neural network using theplurality of training samples. The cascaded neural network may include aplurality of sequentially connected models. The plurality of models mayinclude a first model and at least one second model downstream to thefirst model. In some embodiments, the training module 404 may train thecascade neural network using a deep auto-correct learning strategy. Forexample, the training module 402 may train the models of the cascadedneural network in parallel. As another example, the training module 403may train the models of the cascaded neural network sequentially. Insome embodiments, before training the cascaded neural network, thetraining module 404 may preprocess the training samples by performingimage registration, image resampling, image normalization, and imageconcatenation on the corresponding training samples. More descriptionsregarding the generation of the attenuation correction model may befound elsewhere in the present disclosure (e.g., operation 703 in FIG. 7and the description thereof).

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently, for persons having ordinary skills inthe art, multiple variations and modifications may be conducted underthe teachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.Each of the modules described above may be a hardware circuit that isdesigned to perform certain actions, e.g., according to a set ofinstructions stored in one or more storage media, and/or any combinationof the hardware circuit and the one or more storage media.

In some embodiments, the processing device 120A and/or the processingdevice 120B may share two or more of the modules, and any one of themodules may be divided into two or more units. For instance, theprocessing devices 120A and 120B may share a same acquisition module,that is, the acquisition module 401 and the acquisition module 403 are asame module. In some embodiments, the processing device 120A and/or theprocessing device 1206 may include one or more additional modules, suchas a storage module (not shown) for storing data. In some embodiments,the processing device 120A and the processing device 120B may beintegrated into one processing device 120.

FIG. 5 is a flowchart illustrating an exemplary process 500 forgenerating an attenuation-corrected PET image of a subject according tosome embodiments of the present disclosure. In some embodiments, process500 may be implemented as a set of instructions (e.g., an application)stored in a storage device (e.g., the storage device 130, the storage220, and/or the storage 390). The processing device 120A (e.g., theprocessor 210, the CPU 340, and/or one or more modules illustrated inFIG. 4A) may execute the set of instructions, and when executing theinstructions, the processing device 120A may be configured to performthe process 500. The operations of the illustrated process presentedbelow are intended to be illustrative. In some embodiments, the process500 may be accomplished with one or more additional operations notdescribed and/or without one or more of the operations discussed.Additionally, the order of the operations of process 500 illustrated inFIG. 5 and described below is not intended to be limiting.

In 501, the processing device 120A (e.g., the acquisition module 401)may acquire a PET image and a CT image of a subject. The subject may bebiological or non-biological. For example, the subject may include apatient, a man-made object, etc., as described elsewhere in the presentdisclosure (e.g., FIG. 1 and the descriptions thereof).

In some embodiments, the CT image and/or the PET image may be previouslygenerated and stored in a storage device (e.g., the storage device 130,the storage 220, etc.), and the processing device 120A may retrieve theCT image and/or the PET image from the storage device. Alternatively,the CT image and/or the PET image may be generated by the processingdevice 120A. For example, a PET-CT scanner (e.g., a 2 m PET-CT scanner)may be directed to perform a CT scan and a PET scan on the subject toacquire CT image data and PET image data of the subject. The processingdevice 120A may reconstruct the CT image based on the CT image dataaccording to a CT image reconstruction algorithm. Exemplary CT imagereconstruction algorithms may include a Filter Back Projection (FBP)algorithm, an Algebraic Reconstruction Technique (ART), a LocalReconstruction Algorithm (LocalRA), or the like, or any combinationthereof. The processing device 120A may reconstruct the PET image basedon the PET image data according to a PET image reconstruction algorithm.Exemplary PET image reconstruction algorithms may include an orderedsubset expectation maximization (OSEM) algorithm, a filtered backprojection (FBP) algorithm, a maximum-likelihood reconstruction ofattenuation and activity (MLAA) algorithm, or the like, or anycombination thereof.

In some embodiments, the processing device 120A may reconstruct apreliminary PET image based on the PET image data, and generate the PETimage by performing one or more correction operations (e.g., a randomcorrection, a detector normalization, and/or a scatter correction) otherthan an attenuation correction operation on the preliminary PET image.For example, the detector normalization may be performed to correctvariations in detector sensitivities, thereby reducing or eliminating anartifact caused by the variations in the resulting PET image.

In 503, the processing device 120A (e.g., the generation module 402) maygenerate an attenuation-corrected PET image of the subject based on thePET image and the CT image by application of an attenuation correctionmodel.

As used herein, an attenuation correction model refers to a neuralnetwork model that is configured to receive a PET image and a CT imageand output an attenuation corrected PET image corresponding to the PETimage according to the PET image and the CT image. Anattenuation-corrected PET image corresponding to a PET image refers toan image that is generated by performing an attenuation correction onthe PET image using the attenuation correction model.

In some embodiments, the processing device 120A (e.g., the acquisitionmodule 401) may obtain the attenuation correction model from one or morecomponents of the imaging system 100 (e.g., the storage device 130, theterminals(s) 140) or an external source via a network (e.g., the network150). For example, the attenuation correction model may be previouslytrained by a computing device (e.g., the processing device 120B), andstored in a storage device (e.g., the storage device 130, the storage220, and/or the storage 390) of the imaging system 100. The processingdevice 120A may access the storage device and retrieve the attenuationcorrection model. In some embodiments, the attenuation correction modelmay be generated according to a machine learning algorithm. The machinelearning algorithm may include but not be limited to an artificialneural network algorithm, a deep learning algorithm, a decision treealgorithm, an association rule algorithm, an inductive logic programmingalgorithm, a support vector machine algorithm, a clustering algorithm, aBayesian network algorithm, a reinforcement learning algorithm, arepresentation learning algorithm, a similarity and metric learningalgorithm, a sparse dictionary learning algorithm, a genetic algorithm,a rule-based machine learning algorithm, or the like, or any combinationthereof. The machine learning algorithm used to generate the attenuationcorrection model may be a supervised learning algorithm, asemi-supervised learning algorithm, an unsupervised learning algorithm,or the like. In some embodiments, the attenuation correction model maybe generated by a computing device (e.g., the processing device 120B) byperforming a process (e.g., process 700) for generating an attenuationcorrection model disclosed herein. More descriptions regarding thegeneration of the attenuation correction model may be found elsewhere inthe present disclosure. See, e.g., FIGS. 7-9 and relevant descriptionsthereof.

The attenuation correction model may be of any type of neural networkmodel. For example, the attenuation correction model may be a trainedcascaded neural network including a plurality of trained models that aresequentially connected. The plurality of trained models may include atrained first model and at least one trained second model downstream tothe trained first model. During the application of the attenuationcorrection model, an input of the trained first model may include thePET image and the CT image, and an input of each of the at least onetrained second model may include the PET image, the CT image, and anoutput image of a previous trained model that is upstream and connectedto the trained second model. In some embodiments, a trained model of thetrained cascaded neural network may be a trained convolutional neuralnetwork (CNN) model, a trained generative adversarial network (GAN)model, or any other suitable type of model. Exemplary CNN models mayinclude a Fully Convolutional Network, such as a V-NET model, a U-NETmodel, etc. Exemplary GAN models may include a pix2pix model, aWasserstein GAN (WGAN) model, etc. The trained models of the trainedcascaded neural network may be of the same type or different types. Forexample, each of the trained models may be a CNN model. As anotherexample, one of the trained models may be a CNN model, and the othertrained model(s) may be GAN model(s).

In some embodiments, during the application of the attenuationcorrection model, the processing device 120A may directly input the CTimage and the PET image of the subject into the attenuation correctionmodel, and the attenuation correction model may output theattenuation-corrected PET image of the subject. Alternatively, theprocessing device 120A may need to preprocess the CT image and the PETimage of the subject and/or post-process an output of the attenuationcorrection model to generate the attenuation-corrected PET image. Moredescriptions regarding the generation of the attenuation-corrected PETimage by application of the attenuation correction model may be foundelsewhere in the present disclosure. See, FIG. 6 and the descriptionsthereof.

In some embodiments, the processing device 120A may transmit theattenuation-corrected PET image of the subject to a terminal (e.g., aterminal 140) for display. Optionally, a user of the terminal may inputa response regarding the attenuation-corrected PET image via, forexample, an interface of the terminal. For example, the user mayevaluate whether the attenuation-corrected PET image satisfies a presetcondition (e.g., the quality of the attenuation-corrected PET image issatisfying). According to the evaluation result, the user may send arequest to, for example, adjust scanning parameters of the subject,adjust a pose of the subject during scanning, rescan the subject, repeator redo the attenuation correction, or the like, or any combinationthereof, to the processing device 120A. In some embodiments, operations501 and/or 503 may be performed during or immediately after a PET scanof the subject to achieve an instant browsing of theattenuation-corrected PET image of the subject. For example, by usingthe attenuation correction model, the generation of theattenuation-corrected PET image may cost a time shorter than apredetermined threshold (e.g., 0.5 seconds, 1 second, 2 seconds, 3seconds), such that a user may browse the attenuation-corrected PETimage in real-time or substantially in real-time.

It should be noted that the above description regarding the process 500is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, one or more operations of the process500 may be omitted and/or one or more additional operations may beadded. For example, a storing operation may be added elsewhere in theprocess 500. In the storing operation, the processing device 120A maystore information and/or data (e.g., the attenuation-corrected PETimage, the attenuation correction model, etc.) associated with theimaging system 100 in a storage device (e.g., the storage device 130)disclosed elsewhere in the present disclosure. In some embodiments, onlythe PET image of the subject may be acquired in 501, and theattenuation-corrected PET image may be generated based on the PET imageby application of the attenuation correction model.

FIG. 6 is a flowchart illustrating an exemplary process 600 forgenerating an attenuation-corrected PET image according to someembodiments of the present disclosure. In some embodiments, process 600may be implemented as a set of instructions (e.g., an application)stored in a storage device (e.g., the storage device 130, storage 220,and/or storage 390). The processing device 120A (e.g., the processor210, the CPU 340, and/or one or more modules illustrated in FIG. 4A) mayexecute the set of instructions, and when executing the instructions,the processing device 120A may be configured to perform the process 600.The operations of the illustrated process presented below are intendedto be illustrative. In some embodiments, one or more operations of theprocess 600 may be performed to achieve at least part of operation 503as described in connection with FIG. 5.

In 601, the processing device 120A (e.g., the generation module 402) maypreprocess the CT image and the PET image of the subject.

The preprocessing of the CT image and the PET image may include one ormore image processing operations, such as an image registration, animage denoising, an image enhancement, an image smoothing, an imagetransformation, an image resampling, an image normalization, or thelike, or a combination thereof. The CT image and the PET image may besubjected to same image processing operation(s) or different imageprocessing operations. In some embodiments, the preprocessing of the CTimage and the PET image may include an image registration, an imageresampling, and an image normalization, which may be performedsimultaneously or in any sequence.

Merely by way of example, the processing device 120A may register the CTimage with the PET image. By registration, a registration matrix thatrepresents a transformation relationship between the CT image and thePET image may be generated. According to the registration matrix, acertain pixel (or voxel) of the CT image may be transformed to a sameposition in the registered CT image as a corresponding pixel (or voxel)of the certain pixel in the registered PET image. As used herein, twopixels (or voxel) in two images are regarded as being corresponding toeach other if they represent a same physical point of a same subject.For example, the CT image and the PET image may be registered accordingto an objective function E, which may be represented according toEquation (1) as below:

E=E(M,F(T)),  (1)

where M and F represent two images to be registered, E represents adegree of registration, and T represents a spatial transformationperformed on the image F. According to Equation (1), E may be a functionof the spatial transformation T, that is, E=E(T). In some embodiments,the CT image may be registered with the PET image according to a machinelearning model (e.g., a neural network model, a deep learning model,etc.).

The processing device 120A may further generate a resampled CT image anda resampled PET image by resampling the registered CT image and theregistered PET image. Each of the resampled CT image and the resampledPET image may have a preset image resolution. An image resolution of animage may be measured by, for example, a size and/or count of the pixelsor voxels of the image. For example, the voxel size of each of theresampled CT image and the resampled PET image may be [6 mm, 6 mm, 6mm], [5 mm, 5 mm, 5 mm], or the like. In some embodiments, the presetimage resolution may be associated with the attenuation correctionmodel. For example, the attenuation correction model may be configuredto perform attenuation correction on a PET image with a certain imageresolution. The registered CT image and the registered PET image may beresampled according to the certain image resolution. In someembodiments, the image resampling of the registered CT image and theregistered PET image may be performed according to a resamplingalgorithm, such as a nearest neighbor algorithm, a bilinearinterpolation algorithm, a cubic convolution algorithm, etc.

The processing device 120A may then generate the preprocessed CT imageand the preprocessed PET image by normalizing the resampled CT image andthe resampled PET image. In some embodiments, the normalization of animage may be performed such that pixel (or voxel) values of the imagemay be within a preset range (e.g., [−1, 1]). In some embodiments, theattention correction model may be trained using a plurality of samplePET images and a plurality of sample CT images corresponding to thesample PET images (which will be described in connection with FIG. 7).Each of the resampled CT image and the resampled PET image may benormalized according to Equation (2) as below:

$\begin{matrix}{{I^{\prime} = \frac{I - \mu}{\sigma}},} & (2)\end{matrix}$

where I represents an image to be normalized (e.g., the resampled CTimage or the resampled PET image), and I′ represents a normalized imageof the image I (e.g., the preprocessed CT image or the preprocessed PETimage). When I represents the resampled PET image, μ represents a meanvalue of the sample PET images (or resampled sample PET images), and σrepresents a standard deviation of the sample PET images (or resampledsample PET images). When I represents the resampled CT image, μrepresents a mean value of the sample CT images (or resampled sample CTimages), and σ represents a standard deviation of the sample CT images(or resampled sample CT images). It should be noted that the abovedescription of preprocessing of the PET and CT images is merely providedfor the purposes of illustration, and not intended to limit the scope ofthe present disclosure. For example, the resampling of the PET image (orthe CT image) may be omitted if the image resolution of the PET image(or the CT image) is equal to the preset image resolution. As anotherexample, the image normalization of the resampled CT image and/or theresample PET image may be omitted.

In 603, the processing device 120A (e.g., the generation module 402) maygenerate a concatenated image by concatenating the preprocessed CT imageand the preprocessed PET image. In some embodiments, the preprocessed CTimage and the preprocessed PET image may be concatenated along a presetdimension (e.g., a channel dimension). For example, the preprocessed CTand PET images may both be 2-dimensional images including a firstdimension and a second dimension. The preprocessed CT and PET images maybe concatenated along a third dimension to generate the concatenatedimage (e.g., a 3-dimensional image including the first, second and thirddimensions).

In 605, the processing device 120A (e.g., the generation module 402) mayobtain a preliminary attention-corrected PET image by inputting theconcatenated image into the attenuation correction model. In someembodiments, the attenuation correction model may include a plurality ofsequentially connected trained models, which includes a trained firstmodel and one or more trained second model downstream to the trainedfirst model. The first trained model may be configured to receive theconcatenated image and output an image. Each trained second model may beconfigured to receive the concatenated image and an output image of aprevious trained model connected to the trained second model, and outputan image. The preliminary attention-corrected PET image may be an outputimage of the last second trained model of the attenuation correctionmodel.

In 607, the processing device 120A (e.g., the generation module 402) maygenerate the attenuation-corrected PET image by processing thepreliminary attention-corrected PET image.

In some embodiments, the processing device 120A may generate theattenuation-corrected PET image by performing a denormalizationoperation and/or a resampling operation on the preliminaryattention-corrected PET image. For example, the preliminaryattention-corrected PET image may be denormalized according to Equation(3) as below:

f″=f′*σ _(gt)+μ_(gt),  (3)

where f′ represents an image to be denormalized (e.g., the preliminaryattention-corrected PET image), f″ represents a denormalized image(e.g., a denormalized preliminary attenuation-corrected PET image),σ_(gt) represents a standard deviation of a plurality of sampleattenuation-corrected PET images used in training the attenuationcorrection model, and μ_(gt) represents a mean value of the sampleattenuation-corrected PET images. The processing device 120A may furthergenerate the attenuation-corrected PET image by resampling thedenormalized preliminary attenuation-corrected PET image. Theattenuation-corrected PET image may have a same image resolution as theoriginal PET image.

It should be noted that the above description regarding the process 600is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. The operations of the illustrated process presented beloware intended to be illustrative. In some embodiments, the process 600may be accomplished with one or more additional operations not describedand/or without one or more of the operations discussed. For example,operation 601 may be omitted, and the concatenated image may begenerated by concatenating the original CT image and the original PETimage. As another example, in 605, an output image of the attenuationcorrection model may be designated as the attenuation-corrected PETimage without being processed. Additionally, the order of the operationsof process 600 illustrated in FIG. 6 and described below is not intendedto be limiting. As yet another example, operations 605 and 607 may beintegrated into a single operation.

FIG. 7 is a flowchart illustrating an exemplary process 700 forgenerating an attenuation correction model according to some embodimentsof the present disclosure. In some embodiments, process 700 may beimplemented as a set of instructions (e.g., an application) stored in astorage device (e.g., the storage device 130, storage 220, and/orstorage 390). The processing device 120B (e.g., the processor 210, theCPU 340, and/or one or more modules illustrated in FIG. 4B) may executethe set of instructions, and when executing the instructions, theprocessing device 120B may be configured to perform the process 700. Theoperations of the illustrated process presented below are intended to beillustrative. In some embodiments, the process 700 may be accomplishedwith one or more additional operations not described and/or without oneor more of the operations discussed. Additionally, the order of theoperations of process 700 illustrated in FIG. 7 and described below isnot intended to be limiting. In some embodiments, the attenuationcorrection model described in connection with operation 503 in FIG. 5may be obtained according to the process 700. In some embodiments, theprocess 700 may be performed by another device or system other than theimaging system 100, e.g., a device or system of a vendor of amanufacturer. For illustration purposes, the implementation of theprocess 700 by the processing device 120B is described as an example.

In 701, the processing device 120B (e.g., the acquisition module 403)may acquire a plurality of training samples. Each of the plurality oftraining samples may include a sample PET image of a sample subject, asample CT image of the sample subject, and a sampleattenuation-corrected PET image corresponding to the sample PET image.

As used herein, a sample subject refers to an object that is used fortraining the attenuation correction model. The sample subject may be ofthe same type or a different type of object as the subject as describedin connection with FIG. 5. For example, if the attenuation correctionmodel is used to perform attenuation correction on a PET image of apatient (or a portion thereof), the sample subject may be anotherpatient. A sample PET image and a sample CT image of a sample subjectrefer to a PET image and a CT image of the sample subject. A sampleattenuation-corrected PET image corresponding to a sample PET imagerefers to a ground truth PET image that is generated by performing anattenuation correction on the sample PET image (or sample PET image datacorresponding to the sample PET image) using an attenuation correctiontechnique other than the attenuation correction model disclosed herein.For example, the sample attenuation-corrected PET image may be generatedby a CT-based attenuation correction technique (e.g., a scalingalgorithm, a segmentation algorithm, a Hybrid algorithm, etc.).

In some embodiments, a training sample may be previously generated andstored in a storage device (e.g., the storage device 130, the storage220, the storage 390, or an external database). The processing device120B may retrieve the training sample directly from the storage device.In some embodiments, at least a portion of a training sample may begenerated by the processing device 120B. Merely by way of example, a PETscan and a CT scan may be performed on a sample subject to acquiresample PET image data and sample CT image data of the sample subject.The processing device 120B may acquire the sample PET image data and thesample CT image data of the sample subject from a storage device thatstores the sample PET image data and the sample CT image data. Theprocessing device 120B may reconstruct a sample CT image of the samplesubject based on the sample CT image data, and a sample PET image of thesample subject based on the sample PET image data. The reconstruction ofthe sample CT and PET images of the sample subject may be performed in asimilar manner with that of the CT and PET images of the subject asdescribed in connection with 501, and the descriptions thereof are notrepeated here. Additionally or alternatively, the processing device 120Bmay reconstruct a sample attenuation-corrected PET image of the samplesubject based on the sample CT image and the sample PET image. Duringthe reconstruction of the sample attenuation-corrected PET image, anattenuation correction may be performed on the sample PET image (or thesample PET image data) according to the sample CT image. In someembodiments, the sample attenuation-corrected PET image may bereconstructed according to a reconstruction algorithm, such as a FilterBack Projection (FPB) algorithm, a Maximum Likelihood (MLEM) algorithm,a Least Square (LS) algorithm, a Maximum A Posterior (MAP) algorithm, anOrdered Subsets Expectation Maximization (OSEM) algorithm, etc.

In 703, the processing device 120B (e.g., the training module 404) maygenerate the attenuation correction model by training a cascaded neuralnetwork using the plurality of training samples.

The cascaded neural network may include a plurality of sequentiallyconnected models. The plurality of models may include a first model andat least one second model downstream to the first model. For example,the cascaded neural network may include N models (e.g., a model M1, amodel M2, . . . , and a model Mn), wherein the model M1 may be connectedto the model M2, the model M2 may be connected to the model M3, . . . ,and the model M_(n−1) may be connected to the model Mn. For illustrationpurposes, FIG. 10 illustrates an exemplary cascaded neural network 1000according to some embodiments of the present disclosure. As shown inFIG. 10, the cascaded neural network 1000 may include three sequentiallyconnected models, i.e., a first model 1001, a second model 1002, and asecond model 1003.

In some embodiments, a model of the cascaded neural network may be aconvolutional neural network (CNN) model, a generative adversarialnetwork (GAN) model, or any other suitable type of model. Exemplary CNNmodels may include a Fully Convolutional Network, such as a V-NET model,a U-net model, etc. Exemplary GAN models may include a pix2pix model, aWasserstein GAN (WGAN) model, etc. A GAN model may include a generatorand a discriminator. The generator and the discriminator may competewith each other to reach a balance point at which the generator istrained to generate an image similar to (or almost the same as) a groundtruth image (e.g., a sample attenuation-corrected PET image).

The plurality of models of the cascaded neural network may be of thesame type or different types. For example, each model of the attenuationcorrection model may be a GAN model, in order to improve the correctionaccuracy and generate an attenuation-corrected PET image with a highimage quality. In some embodiments, one or more of the models of thecascaded neural network may be a CNN model. The depth of each CNN modelmay be smaller than a threshold in order to reduce the number (count) ofgenerated feature maps on the premise of model accuracy. This may reducethe required processing time of the resulting attenuation correctionmodel in application and achieve a rapid PET image reconstruction. Insome embodiments, the cascaded neural network may include one or moreadditional components, such as a skip-connection, a residual block, adense block, or the like, or any combination thereof. Such additionalcomponents may be configured to combine different features extracted bydifferent layers (or components) of the cascaded neural network, therebyaccelerating convergence during model training and improving theaccuracy of the resulting attenuation correction model.

In some embodiments, the cascaded neural network may include one or moremodel parameters. The processing device 120B may initialize parametervalue(s) of the model parameter(s) before training, and the value(s) ofthe model parameter(s) of the cascaded neural network may be updatedduring the training of the cascaded neural network. Exemplary modelparameters of the cascaded neural network may include the number (count)of the models in the cascaded neural network, model parameters of eachof the models, a loss function, or the like, or any combination thereof.Taking a CNN model of the cascaded neural network as an example,exemplary model parameters of the CNN model may include the number (orcount) of convolutional layers, the number (or count) of kernels, akernel size, a stride, a padding of each convolutional layer, or thelike, or any combination thereof. In some embodiments, the kernel size,the stride, and the padding of a convolutional layer of the CNN modelmay be equal to 3, 1, and 1, respectively.

In some embodiments, the cascaded neural network may be trained using adeep auto-context learning strategy. By using the deep auto-contextlearning strategy, a difference between an output image of each modeland the ground truth attenuation-corrected PET image may be reducedgradually, thereby generating an attenuation correction model with ahigh reliability. In addition, the training samples (e.g., the samplePET images and the sample CT images) may be inputted into each of themodels during the training process. This may avoid a loss of image datadue to operations (e.g., a convolutional operation) performed intraining.

For example, the models of the cascaded neural network may be trained inparallel during the training of the cascaded neural network. Merely byway of example, the processing device 120B may train the cascaded neuralnetwork by iteratively and jointly updating the parameters of each modelof the cascaded neural network based on the training samples. In someembodiments, the training of the cascaded neural network may include oneor more iterations, wherein at least one of the iteration(s) may includeone or more operations of process 800 as described in connection withFIG. 8.

Alternatively, the models of the cascaded neural network may besequentially trained during the training of the cascaded neural network.In some embodiments, the first model may be trained using the pluralityof training samples. Each of the at least one second model may betrained using the plurality of training samples and one or more trainedmodels generated before the training of the second model. For example, amodel M_(i) (i being greater than 1) of the cascaded neural network maybe trained based on the training samples and the trained models M1 toM_(i−1) that are generated before the training of the model M_(i). Moredescriptions regarding the training of a second model may be foundelsewhere in the present disclosure. See, e.g., FIG. 9 and relevantdescriptions thereof.

In some embodiments, the training samples (or a portion thereof) mayneed to be preprocessed before being used in training the cascadedneural network. For example, for a training sample, the processingdevice 120B may generate a sample concentrated image by performing imageregistration, image resampling, image normalization, and imageconcatenation on the corresponding sample PET image and the sample CTimage. The generation of a sample concatenated image may be performed ina similar manner with that of a concatenated image as described inoperation 603, and the descriptions thereof are not repeated here. Theprocessing device 120B may also perform image resampling and/or imagenormalization on the sample attenuation-corrected PET image of thetraining sample to generate a preprocessed sample attenuation-correctedPET image. The processing device 120B may further generate theattenuation correction model by training the cascaded neural networkusing the sample concatenated image and the preprocessed sampleattenuation-corrected PET image of each training sample.

In some embodiments, the training samples (or a portion thereof) may bepreviously preprocessed and stored in a storage device (e.g., thestorage device 130, the storage 220, the storage 390, or an externaldatabase). For example, the sample CT image, the sample PET image,and/or the sample attenuation-corrected PET image of a training samplemay be images that have been preprocessed and stored in the storagedevice. The processing device 120B may retrieve the training sample fromthe storage device, and apply the training sample in training thecascaded neural network without preprocessing the training sample. Insome embodiments, the sample CT image and the sample PET image of atraining sample may be stored in the storage device as a correspondingsample concatenated image.

It should be noted that the above description regarding process 700 ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, one or more operations may be added oromitted. For example, the attenuation correction model may be stored ina storage device (e.g., the storage device 130) disclosed elsewhere inthe present disclosure for further use (e.g., in attenuation correctionof a PET image as described in connection with FIG. 5). As anotherexample, after the attenuation correction model is generated, theprocessing device 1206 may further test the attenuation correction modelusing a set of testing images. Additionally or alternatively, theprocessing device 120B may update the attenuation correction modelperiodically or irregularly based on one or more newly-generatedtraining images (e.g., new sample PET images, new sample CT images, andnew sample attenuation-corrected PET images generated in medicaldiagnosis).

FIG. 8 is a flowchart illustrating an exemplary process 800 for traininga cascaded neural network according to some embodiments of the presentdisclosure. In some embodiments, process 800 may be implemented as a setof instructions (e.g., an application) stored in a storage device (e.g.,the storage device 130, storage 220, and/or storage 390). The processingdevice 120B (e.g., the processor 210, the CPU 340, and/or one or moremodules illustrated in FIG. 4B) may execute the set of instructions, andwhen executing the instructions, the processing device 120B may beconfigured to perform process 800. In some embodiments, one or moreoperations of process 800 may be performed to achieve at least part ofoperation 703 as described in connection with FIG. 7. For example, theprocess 800 may be performed to achieve a current iteration in trainingthe cascaded neural network, during which the models of the cascadedneural network are trained in parallel. The current iteration may beperformed based on at least some of the training samples (or referred toas first training samples). In some embodiments, a same set or differentsets of first training samples may be used in different iterations intraining the cascaded neural network.

In 801, for each of the first training samples, the processing device120B (e.g., the training module 404) may generate a predictedattenuation-corrected PET image by application of an updated cascadedneural network determined in a previous iteration.

During the application of the updated cascaded neural network on a firsttraining sample, the first model of the updated cascaded neural networkmay be configured to receive the first training sample, and each secondmodel of the updated cascaded neural network may be configured toreceive the first training sample and an output image of a previousmodel that is upstream and connected to the second model in the updatedcascaded neural network. The predicted attenuation-corrected PET imagemay be an output image of the last second model of the sequentiallyconnected models in the updated cascaded neural network. As used herein,“receiving a training sample” refers to receiving the (preprocessed)sample CT image and the (preprocessed) sample PET image of the trainingsample. In some embodiments, an input of a model may include a pluralityof images. The images may be directly inputted into the model, or beconcatenated into a concatenated image and inputted into the model. Forexample, the input of the first model may include a sample concatenatedimage generated based on the sample CT image and the sample PET image ofthe first training sample. The input of a second model may include aconcatenated image generated based on the sample concatenated image andthe output image of the previous model connected to the second model.

For example, an updated cascaded neural network (denoted as A) of thecascaded neural network 1000 as illustrated in FIG. 10 may be determinedin the previous iteration. A first training sample may include a sampleCT image 1011, a sample PET image 1012, and a sample attention-correctedPET image 1013. The sample CT image 1011 and the sample PET image 1012may be preprocessed and concatenated to generate a sample concatenatedimage, i.e., an input 1100. The input 1100 may be inputted to the firstmodel 1001 of the updated cascaded neural network A to generate anoutput image 1014. The output image 1014 and the input 1100 may beconcatenated to generate an input 1200. The input 1200 may be inputtedto the second model 1002 of the updated cascaded neural network A togenerate an output image 1015. The output image 1015 and the input 1100may be concatenated to generate an input 1300. The input 1300 may beinputted to the second model 1003 to generate an output image, i.e., thepredicted-attenuation-corrected PET image 1016.

In 803, the processing device 120B (e.g., the training module 404) maydetermine, based on the predicted attenuation-corrected PET image andthe sample attenuation-corrected PET image of each first trainingsample, an assessment result of the updated cascaded neural network.

The assessment result may indicate an accuracy and/or efficiency of theupdated cascaded neural network. Taking the updated cascaded neuralnetwork A aforementioned as an example, the assessment result may beassociated with a difference between the predicted attenuation-correctedPET image 1016 and the sample attenuation-corrected PET image 1013 ofeach first training sample. For example, a value of an overall lossfunction, such as an L1 loss, an L2 loss, or a smooth L1 loss, may bedetermined to measure an overall difference between the predictedattenuation-corrected PET image 1016 and the sampleattenuation-corrected PET image 1013 of each first training sample. Theprocessing device 120B may determine the assessment result based on thevalue of the overall loss function. As another example, for each modelin the updated cascaded neural network, the processing device 120B maydetermine a value of a loss function corresponding to the model based onthe sample attenuation-corrected PET image 1013 and an output image ofthe model corresponding to each first training sample. Merely by way ofexample, as represented by dotted boxes in FIG. 10, the sampleattenuation-corrected PET image 1013 and the output image 1014 may beused to determine a value of the loss function corresponding to thefirst model 1001. The sample attenuation-corrected PET image 1013 andthe output image 1015 may be used to determine a value of the lossfunction corresponding to the second model 1002. The processing device120B may determine the assessment result based on the values of the lossfunctions of the models in the updated cascaded neural network A.Additionally or alternatively, the assessment result may be associatedwith a time needed for the updated cascaded neural network A to generatethe predicted attenuation-corrected PET image 1016 of each firsttraining sample. For example, the short the needed time is, the highefficiency the updated cascaded neural network A has.

In some embodiments, the assessment result may include a determinationas to whether a termination condition is satisfied in the currentiteration. In some embodiments, the termination condition may relate tothe value of the overall loss function and/or the value of the lossfunction of each model in the updated cascaded neural network. Forexample, the termination condition may be satisfied if the value of theoverall loss function is minimal or smaller than a threshold (e.g., aconstant). As another example, the termination condition may besatisfied if the value of the overall loss function converges. In someembodiments, convergence may be deemed to have occurred if, for example,the variation of the values of the overall loss function in two or moreconsecutive iterations is equal to or smaller than a threshold (e.g., aconstant), a certain count of iterations may be performed, or the like.In some embodiments, the termination condition may be satisfied if atime needed for the updated cascaded neural network to generate thepredicted attenuation-corrected PET image of each first training sampleis smaller than a threshold.

In some embodiments, in response to a determination that the terminationcondition is satisfied, the processing device 120B may determine theupdated cascaded neural network as the attenuation correction model. Inresponse to a determination that the termination condition is notsatisfied, the processing device 120B may proceed to 805, in which theprocessing device 120B (e.g., the training module 404) may update theparameter values of the updated cascaded neural network to be used in anext iteration based on the assessment result.

For example, the processing device 120B may update the parametervalue(s) of each model in the updated cascaded neural network based onthe value of the overall loss function according to, for example, abackpropagation algorithm. As another example, for each of the models inthe updated cascaded neural network, the processing device 120B mayupdate the parameter value(s) of the model based on the value of thecorresponding loss function and optionally the value(s) the lossfunction(s) of the model(s) downstream to the model. In someembodiments, a model may include a plurality of parameter values, andupdating parameter value(s) of the model refers to updating at least aportion of the parameter values of the model.

FIG. 9 is a flowchart illustrating an exemplary process 900 for traininga second model of a cascaded neural network according to someembodiments of the present disclosure. In some embodiments, process 900may be implemented as a set of instructions (e.g., an application)stored in a storage device (e.g., the storage device 130, storage 220,and/or storage 390). The processing device 120B (e.g., the processor210, the CPU 340, and/or one or more modules illustrated in FIG. 4B) mayexecute the set of instructions, and when executing the instructions,the processing device 120B may be configured to perform the process 900.In some embodiments, one or more operations of the process 900 may beperformed to achieve at least part of operation 703 as described inconnection with FIG. 7. For example, the cascaded neural network mayinclude a plurality of sequentially connected models including a firstmodel and at least one second model downstream to the first model. Theplurality of models may be trained in sequence. The process 900 may beperformed for training a second model of the cascaded neural network.

In 901, for each of the plurality of training samples, the processingdevice 120B (e.g., the training module 404) may generate a preliminaryimage by application of the one or more trained models generated beforethe training of the second model. The preliminary image may be an outputimage of a last model of the one or more trained models that is upstreamand connected to the updated second model.

For example, if the second model to be trained is a model Mi of thecascaded neural network, the processing device 120B may generate apreliminary image corresponding to a training sample by application ofthe trained model M1 to M_(i−1). Merely by way of example, a sample PETimage and a sample CT image of the training sample may be inputted intoa model including the trained models M1 to M_(i−1) to generate apreliminary attenuation-corrected sample PET image (referred to as apreliminary image for brevity).

The processing device 120 may further train the second model using thetraining samples and the corresponding preliminary images. For example,the processing device 120B may initialize parameter values of the secondmodel before training the second model or before training the cascadedneural network. The processing device 120B may train the second model byiteratively updating the parameter values of the second model based onthe training samples and corresponding preliminary images. In someembodiments, the training of the second model may include one or moresecond iterations. For illustration purposes, a current second iterationincluding operations 903-907 of the process 900 is describedhereinafter. The current second iteration may be performed based on atleast some of the training samples (or referred to as second trainingsamples). The second training samples may include one or more sametraining samples as or different training samples from the firsttraining samples as described in connection with FIG. 8. In someembodiments, a same set or different sets of second training samples maybe used in different second iterations in training the second model.

In 903, for each of the second training samples, the processing device120B (e.g., the training module 404) may generate an output image byinputting the training sample and the corresponding preliminary imageinto the updated second model.

For example, for a second training sample, the sample CT image, thesample PET image, and the corresponding preliminary image of the secondtraining sample may be concatenated to generate a concatenated image,which may be inputted to the updated second model.

In 905, the processing device 120B (e.g., the training module 404) maydetermine, based on the output image of the updated second model and thesample attenuation-corrected PET image corresponding to each secondtraining sample, a second assessment result.

The second assessment result may indicate an accuracy and/or efficiencyof the updated second model. In some embodiments, the second assessmentresult may be associated with a difference between the output image ofthe updated second model and the sample attenuation-corrected PET imagecorresponding to each second training sample. For example, theprocessing device 120B may determine a value of a second loss function(e.g., a L1 loss, a L2 loss, or a smooth L1 loss) corresponding to theupdated second model based on the output image of the updated secondmodel and the sample attenuation-corrected PET image corresponding toeach second training samples. Additionally or alternatively, the secondassessment result may be associated with a second time needed for theupdated second network to generate the output image of each of the atleast some of the plurality of training samples. For example, theshorter the second needed time is, the higher efficiency the updatedsecond model has.

In some embodiments, the processing device 120B may determine the secondassessment result based on the value of the second loss function and/orthe second needed time. The second assessment result may include adetermination as to whether a second termination condition is satisfiedin the current second iteration. The determination of the secondassessment result may be performed in a similar manner with that of thefirst assessment result as described in connection with FIG. 8, and thedescriptions thereof are not repeated here.

In some embodiments, in response to a determination that the secondtermination condition is satisfied, the processing device 120B maydetermine the updated second model as the trained second model. Inresponse to a determination that the second termination condition is notsatisfied, the processing device 120B may proceed to 907, in which theprocessing device 120B (e.g., the training module 404) may update theparameter values of the updated second model to be used in a nextiteration based on the second assessment. For example, the processingdevice 120B may update the parameter value(s) of the updated secondmodel based on the value of the second loss function according to, forexample, a backpropagation algorithm.

It should be noted that the above descriptions regarding the processes800 and 900 are merely provided for the purposes of illustration, andnot intended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, multiple variations and modificationsmay be made under the teachings of the present disclosure. However,those variations and modifications do not depart from the scope of thepresent disclosure. The operations of the illustrated process presentedabove are intended to be illustrative. In some embodiments, the process800 and/or process 900 may be accomplished with one or more additionaloperations not described and/or without one or more of the operationsdiscussed. Additionally, the order of the operations of the process 800and/or the process 900 is not intended to be limiting. For example, inprocess 900, the processing device 120B may further test the trainedsecond model using a set of testing samples to determine whether atesting condition is satisfied. If the testing condition is notsatisfied, the process 900 may be performed again to further train themodel.

FIG. 11A illustrates an exemplary CT image 1101 of a portion of apatient according to some embodiments of the present disclosure. FIG.11B illustrates an exemplary PET image 1102 of the portion of thepatient in FIG. 11A according to some embodiments of the presentdisclosure. FIG. 11C illustrates an exemplary attenuation-corrected PETimage 1103 corresponding to the PET image 1102 according to someembodiments of the present disclosure. The attenuation-corrected PETimage 1103 was generated by application an attenuation correction modeldisclosed in the present disclosure on the CT image 1101 and the PETimage 1102. For example, the CT image 1101 and the PET image 1102 may beinputted to the attenuation correction model. The attenuation correctionmodel may output the attenuation-corrected PET image 1103.

FIG. 12A illustrates an exemplary CT image 1201 of a portion of apatient according to some embodiments of the present disclosure. FIG.12B illustrates an exemplary PET image 1202 of the portion of thepatient in FIG. 12A according to some embodiments of the presentdisclosure, respectively. FIG. 12C illustrates an exemplaryattenuation-corrected PET image 1203 corresponding to the PET image 1202according to some embodiments of the present disclosure. Theattenuation-corrected PET image 1203 was generated by application anattenuation correction model disclosed in the present disclosure on theCT image 1201 and the PET image 1202. For example, the CT image 1201 andthe PET image 1202 may be inputted to the attenuation correction model.The attenuation correction model may output the attenuation-correctedPET image 1203.

FIG. 13A illustrates an exemplary CT image 1301 of a portion of apatient according to some embodiments of the present disclosure. FIG.13B illustrates an exemplary PET image 1302 of the portion of thepatient in FIG. 13A according to some embodiments of the presentdisclosure. FIG. 13C illustrates an exemplary attenuation-corrected PETimage 1303 corresponding to the PET image 1302 according to someembodiments of the present disclosure. The attenuation-corrected PETimage 1303 was generated by application an attenuation correction modeldisclosed in the present disclosure on the CT image 1301 and the PETimage 1302. For example, the CT image 1301 and the PET image 1302 may beinputted to the attenuation correction model. The attenuation correctionmodel may output the attenuation-corrected PET image 1303.

As shown in FIGS. 11A-13C, the attenuation-corrected model may be usedto correct a PET image to reconstruct an attenuation-corrected PET imagewith a relatively higher speed and resolution.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer-readable media having computer-readableprogram code embodied thereon.

A non-transitory computer-readable signal medium may include apropagated data signal with computer readable program code embodiedtherein, for example, in baseband or as part of a carrier wave. Such apropagated signal may take any of a variety of forms, includingelectromagnetic, optical, or the like, or any suitable combinationthereof. A computer-readable signal medium may be any computer-readablemedium that is not a computer-readable storage medium and that maycommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.Program code embodied on a computer-readable signal medium may betransmitted using any appropriate medium, including wireless, wireline,optical fiber cable, RF, or the like, or any suitable combination of theforegoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object-oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran, Perl, COBOL,PHP, ABAP, dynamic programming languages such as Python, Ruby, andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations, therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose and that the appended claimsare not limited to the disclosed embodiments, but, on the contrary, areintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the disclosed embodiments. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as asoftware-only solution, e.g., an installation on an existing server ormobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereofto streamline the disclosure aiding in the understanding of one or moreof the various inventive embodiments. This method of disclosure,however, is not to be interpreted as reflecting an intention that theclaimed object matter requires more features than are expressly recitedin each claim. Rather, inventive embodiments lie in less than allfeatures of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities, properties, andso forth, used to describe and claim certain embodiments of theapplication are to be understood as being modified in some instances bythe term “about,” “approximate,” or “substantially.” For example,“about,” “approximate” or “substantially” may indicate ±20% variation ofthe value it describes, unless otherwise stated. Accordingly, in someembodiments, the numerical parameters set forth in the writtendescription and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting effect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

What is claimed is:
 1. A system for image correction in positronemission tomography (PET), comprising: at least one storage deviceincluding a set of instructions; and at least one processor configuredto communicate with the at least one storage device, wherein whenexecuting the set of instructions, the at least one processor isconfigured to direct the system to perform operations including:acquiring a PET image and a computed tomography (CT) image of a subject;and generating, based on the PET image and the CT image, anattenuation-corrected PET image of the subject by application of anattenuation correction model, wherein: the attenuation correction modelis a trained cascaded neural network including a plurality of trainedmodels that are sequentially connected, the plurality of trained modelsinclude a trained first model and at least one trained second modeldownstream to the trained first model, and during the application of theattenuation correction model, an input of each of the at least onetrained second model includes the PET image, the CT image, and an outputimage of a previous trained model that is upstream and connected to thetrained second model.
 2. The system of claim 1, wherein to generate anattenuation-corrected PET image of the subject by application of anattenuation correction model, the at least one processor is configuredto direct the system to perform the operations including: preprocessingthe CT image and the PET image; generating a concatenated image byconcatenating the preprocessed CT image and the preprocessed PET image;obtaining a preliminary attenuation-corrected PET image by inputting theconcatenated image into the attenuation correction model; and generatingthe attenuation-corrected PET image by processing the preliminaryattenuation-corrected PET image.
 3. The system of claim 2, wherein topreprocess the CT image and the PET image, the at least one processor isfurther configured to direct the system to perform the operationsincluding: registering the CT image with the PET image; generating aresampled CT image and a resampled PET image by resampling theregistered CT image and the registered PET image, each of the resampledCT image and the resampled PET image having a preset image resolution;and generating the preprocessed CT image and the preprocessed PET imageby normalizing the resampled CT image and the resampled PET image. 4.The system of claim 3, wherein to generate the attenuation-corrected PETimage by processing the preliminary attenuation-corrected PET image, theat least one processor is further configured to direct the system toperform the operations including: denormalizing the preliminaryattenuation-corrected PET image; and generating theattenuation-corrected PET image by resampling the denormalizedpreliminary attenuation-corrected PET image, the attenuation-correctedPET image and the PET image having a same image resolution.
 5. Thesystem of claim 4, wherein: the attenuation correction model is trainedusing a plurality of sample attenuation-corrected PET images, and thedenormalization of the preliminary attenuation-corrected PET image isperformed based on a mean value and a standard deviation of theplurality of sample attenuation-corrected PET images.
 6. The system ofclaim 1, wherein to acquire a PET image and a CT image of a subject, theat least one processor is further configured to direct the system toperform the operations including: acquiring CT image data and PET imagedata of the subject by performing a CT scan and a PET scan of thesubject; reconstructing, based on the CT image data, the CT image;reconstructing, based on the PET image data, a preliminary PET image;and generating the PET image by performing a random correction and adetector normalization on the preliminary PET image.
 7. The system ofclaim 1, wherein at least one of the plurality of trained models is aconvolutional neural network (CNN) model or a generative adversarialnetwork (GAN) model.
 8. The system of claim 1, wherein the generationthe attenuation-corrected PET image of the subject is performed within 1second.
 9. A system for generating an attenuation correction model,comprising: at least one storage device including a set of instructions;and at least one processor configured to communicate with the at leastone storage device, wherein when executing the set of instructions, theat least one processor is configured to direct the system to performoperations including: acquiring a plurality of training samples, each ofthe plurality of training samples including a sample positron-emissiontomography (PET) image of a sample subject, a sample computed tomography(CT) image of the sample subject, and a sample attenuation-corrected PETimage corresponding to the sample PET image; and generating theattenuation correction model by training a cascaded neural network usingthe plurality of training samples, wherein: the cascaded neural networkincludes a plurality of sequentially connected models, the plurality ofmodels includes a first model and at least one second model downstreamto the first model, and during the training of the cascaded neuralnetwork, each of the at least one second model is trained based on theplurality training samples and one or more models in the cascaded neuralnetwork upstream to the second model.
 10. The system of claim 9, whereinthe plurality of models are trained in parallel during the training ofthe cascaded neural network, and the training the cascaded neuralnetwork includes: initializing parameter values of the cascaded neuralnetwork; and training the cascaded neural network by iterativelyupdating the parameter values of the cascaded neural network based onthe plurality of training samples.
 11. The system of claim 10, whereiniteratively updating the parameter values of the cascaded neural networkincludes performing an iterative operation including one or moreiterations, and each of at least one iteration of the iterativeoperation includes: for each of at least some of the plurality oftraining samples, generating a predicted attenuation-corrected PET imageby application of an updated cascaded neural network determined in aprevious iteration; determining, based on the predictedattenuation-corrected PET image and the sample attenuation-corrected PETimage of each of the at least some of the plurality of training samples,an assessment result of the updated cascaded neural network; and furtherupdating the parameter values of the updated cascaded neural network tobe used in a next iteration based on the assessment result, whereinduring the application of the updated cascaded neural network to atraining sample, each second model of the updated cascaded neuralnetwork is configured to receive the training sample and an output imageof a previous model that is upstream and connected to the second modelin the updated cascaded neural network, and the predictedattenuation-corrected PET image is an output image of a last secondmodel of the sequentially connected models in the updated cascadedneural network.
 12. The system of claim 10, wherein the assessmentresult is determined based on at least one of: a difference between thepredicted attenuation-corrected PET image and the sampleattenuation-corrected PET image of each of at least some of theplurality of training samples, or a time needed for the updated cascadedneural network to generate the predicted attenuation-corrected PET imageof each of the at least some of the plurality of training samples. 13.The system of claim 11, wherein the determining an assessment result ofthe updated cascaded neural network comprises: for each of the pluralityof models in the updated cascaded neural network, determining, based onthe sample attenuation-corrected PET image and an output image of themodel corresponding to each of the at least some of the plurality oftraining samples, a value of a loss function corresponding to the model;and determining, based on the values of the loss functions of theplurality of models, the assessment result.
 14. The system of claim 13,wherein the parameter values of the cascaded neural network includeparameter values of each of the plurality of models, and the furtherupdating the parameter values of the updated cascaded neural networkbased on the assessment result comprises: for each of the plurality ofmodels in the updated cascaded neural network, updating the parametervalues of the model based on the value of the corresponding lossfunction.
 15. The system of claim 9, wherein: the training the cascadedneural network comprises sequentially training the plurality of models,the first model is trained using the plurality of training samples, andeach of the at least one second model is trained using the plurality oftraining samples and one or more trained models generated before thetraining of the second model.
 16. The system of claim 15, wherein foreach of the at least one second model, the training the second modelincludes: for each of the plurality of training samples, generating apreliminary image by application of the one or more trained modelsgenerated before the training of the second model; initializingparameter values of the second model; and training the second model byiteratively updating the parameter values of the second model based onthe plurality of training samples and the corresponding preliminaryimages.
 17. The system of claim 16, wherein the training the secondmodel includes a second iterative operation including one or moreiterations, each of at least one iteration of the second iterativeoperation including: for each of at least some of the plurality oftraining samples, generating an output image of the second model byinputting the training sample and the corresponding preliminary imageinto an updated second model determined in a previous iteration;determining, based on the output image of the updated second model andthe sample attenuation-corrected PET image corresponding to each of theat least some of the plurality of training samples, a second assessmentresult; and further updating the parameter values of the updated secondmodel to be used in a next iteration based on the second assessmentresult.
 18. The system of claim 9, wherein to generate the attenuationcorrection model by training a cascaded neural network using theplurality of training samples, the at least one processor is furtherconfigured to direct the system to perform the operations including: foreach of the plurality of training samples, preprocessing the sample PETimage, the sample CT image, and the sample attenuation-corrected PETimage of the training sample; and generating a sample concentrated imageby concatenating the preprocessed sample CT image and the preprocessedsample PET image of the training sample; and generating the attenuationcorrection model by training the cascaded neural network using thesample concatenated images and the plurality of preprocessed sampleattenuation-corrected PET images.
 19. The system of claim 9, wherein atleast one of the plurality of models is a convolutional neural network(CNN) model or a generative adversarial network (GAN) model.
 20. Amethod for image correction in positron emission tomography (PET)implemented on a computing device having at least one processor and atleast one storage device, the method comprising: acquiring a PET imageand a computed tomography (CT) image of a subject; generating, based onthe PET image and the CT image, an attenuation-corrected PET image ofthe subject by application of an attenuation correction model, wherein:the attenuation correction model is a trained cascaded neural networkincluding a plurality of trained models that are sequentially connected,the plurality of trained models include a trained first model and atleast one trained second model downstream to the trained first model,and during the application of the attenuation correction model, an inputof each of the at least one trained second model includes the PET image,the CT image, and an output image of a previous trained model that isupstream and connected to the trained second model, and transmitting theattenuation-corrected PET image of the subject to a terminal fordisplay.